In the software development context, it is crucial to conduct testing of applications within a testing environment before they are deployed into the broader-scale production environment. To this end, conventional testing methods will often use dummy data in lieu of real-world data to test the functionality of the applications.
That said, there are a number of technical problems with this type of conventional testing method. In particular, the dummy data used may not be fully reflective of the type of data that will actually be processed in the production environment, which may obscure coding errors during testing until they become apparent after the application has already been deployed. On the other hand, real-world data may be subject to security and privacy concerns and thus cannot be freely used within the testing environment. Furthermore, the database schemas may not necessarily match between the production database and the development database, requiring user intervention. Finally, conventional systems often continuously generate testing data without regard to whether there is an immediate need for the testing data, which leads to computing inefficiencies.
Accordingly, there is a need for a secure way to provide testing data that is as close to real-world data as possible.